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  "markdown": "Gemma Product Manager.\n\n- LinkedIn: https://uk.linkedin.com/in/lgusmartins\n- X: https://x.com/gusthema\n\n## What Gus builds\n\n- [DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/tools/diffusiongemma-26b-a4b-it.md), graded D, 50/100, in Open-weight models. DiffusionGemma 26B A4B IT is Google DeepMind's experimental open-weight text diffusion model on the Gemma 4 26B A4B mixture-of-experts architecture, 25.2B parameters with 3.8B active, taking text, images and video and writing text in 256-token blocks, under Apache 2.0. Gemma Product Manager, researched 2026-10-27.\n- [EmbeddingGemma 300M](https://www.anchorterminal.com/tools/embeddinggemma-300m.md), graded D, 50.7/100, in Embeddings \u0026 rerankers. EmbeddingGemma 300M is Google DeepMind's open-weight text embedding model of 308M parameters, returning 768-dimension vectors that truncate to 512, 256 or 128 from up to 2,048 tokens in over 100 languages, gated under the Gemma Terms of Use. Gemma Product Manager, researched 2026-10-27.\n- [Gemma 4 12B IT](https://www.anchorterminal.com/tools/gemma-4-12b-it.md), graded BB, 71.3/100, in Open-weight models. Gemma 4 12B IT is Google DeepMind's open-weight instruction-tuned dense model, 11.95B parameters in an encoder-free design, taking text, images, video frames and audio with a 262,144-token context, published on Hugging Face under Apache 2.0. Gemma Product Manager, researched 2026-10-26.\n- [Gemma 4 26B A4B IT](https://www.anchorterminal.com/tools/gemma-4-26b-a4b-it.md), graded BB, 72.2/100, in Open-weight models. Gemma 4 26B A4B IT is Google DeepMind's open-weight instruction-tuned mixture-of-experts model, 25.2B parameters with 3.8B active, taking text and images with a 262,144-token context, published on Hugging Face under Apache 2.0. Gemma Product Manager, researched 2026-10-26.\n- [Gemma 4 31B IT](https://www.anchorterminal.com/tools/gemma-4-31b-it.md), graded BB, 74.1/100, in Open-weight models. Gemma 4 31B IT is Google DeepMind's open-weight dense multimodal model of 30.7B parameters, taking text and images with a 256K-token context, published on Hugging Face in BF16 with 4-bit QAT builds under Apache 2.0. Gemma Product Manager, researched 2026-10-26.\n- [Gemma 4 E2B IT](https://www.anchorterminal.com/tools/gemma-4-e2b-it.md), graded C, 60.4/100, in Open-weight models. Gemma 4 E2B IT is Google DeepMind's small open-weight instruction-tuned model for phones and laptops, 2.3B effective parameters (5.1B with per-layer embeddings), taking text, images, video frames and audio with a 131,072-token context, published on Hugging Face under Apache 2.0. Gemma Product Manager, researched 2026-10-26.\n- [Gemma 4 E4B IT](https://www.anchorterminal.com/tools/gemma-4-e4b-it.md), graded B, 63.2/100, in Open-weight models. Gemma 4 E4B IT is Google DeepMind's small open-weight instruction-tuned model for phones and laptops, 4.5B effective parameters (8B with per-layer embeddings), taking text, images, video frames and audio with a 131,072-token context, published on Hugging Face under Apache 2.0. Gemma Product Manager, researched 2026-10-26.\n\n## Works with\n\n- [Brendan O’Donoghue](https://www.anchorterminal.com/people/brendan-o-donoghue/), Director of Research; DiffusionGemma / Gemini Diffusion lead\n- [Min Choi](https://www.anchorterminal.com/people/min-choi/), EmbeddingGemma launch product manager; current Gemini models PM\n- [Sahil Dua](https://www.anchorterminal.com/people/sahil-dua/), EmbeddingGemma research lead; launch lead research engineer\n- [Sebastian Flennerhag](https://www.anchorterminal.com/people/sebastian-flennerhag/), Research Scientist; DiffusionGemma research lead\n\n## Questions\n\n### Who is Gus Martins?\n\nGus Martins is gemma Product Manager.\n\n### What does Google DeepMind make?\n\nAnchor Terminal lists DiffusionGemma 26B A4B IT, EmbeddingGemma 300M, Gemma 4 12B IT, Gemma 4 26B A4B IT, Gemma 4 31B IT, Gemma 4 E2B IT and Gemma 4 E4B IT from Google DeepMind. DiffusionGemma 26B A4B IT is Google DeepMind's experimental open-weight text diffusion model on the Gemma 4 26B A4B mixture-of-experts architecture, 25.2B parameters with 3.8B active, taking text, images and video and writing text in 256-token blocks, under Apache 2.0.\n\n### How are Gus's tools graded?\n\nDiffusionGemma 26B A4B IT is graded D, 50 out of 100; EmbeddingGemma 300M is graded D, 50.7 out of 100; Gemma 4 12B IT is graded BB, 71.3 out of 100; Gemma 4 26B A4B IT is graded BB, 72.2 out of 100; Gemma 4 31B IT is graded BB, 74.1 out of 100; Gemma 4 E2B IT is graded C, 60.4 out of 100; Gemma 4 E4B IT is graded B, 63.2 out of 100, on Anchor Terminal's benchmark, which scores every listing against the same checklist.\n\n## Sources\n\n- https://uk.linkedin.com/in/lgusmartins\n- https://x.com/gusthema\n- https://blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation/\n- https://arxiv.org/html/2608.00146v1\n- https://x.com/flennerhag\n- https://x.com/bodonoghue85\n- https://gemma.ai/\n- https://x.com/Google\n- https://deepmind.google/\n- https://ai.google.dev/gemma/docs/discord\n- https://gemma.ai/resources/\n- https://developers.googleblog.com/en/introducing-embeddinggemma/\n- https://arxiv.org/abs/2509.20354\n- https://x.com/sahildua2305\n- https://www.linkedin.com/in/minschoi\n- https://x.com/iohcsnim\n- https://huggingface.co/google/embeddinggemma-300m\n\nThese are the people building the tools we list, named for their public role in the company or project behind a listing, from public pages, with the date it was researched. They don't work for Anchor Terminal (our own team is on the about page, /about/#author). We show public roles, public profiles and what the listing pages show, nothing private. To correct something, send an about or a photo, or have a page removed, write to privacy@anchorterminal.com.\n",
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